CN112927042A - Hotel self-service shopping settlement platform and system - Google Patents
Hotel self-service shopping settlement platform and system Download PDFInfo
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Abstract
The invention discloses a hotel self-help shopping settlement platform and a system, wherein the hotel self-help shopping settlement platform comprises: a human-machine interface module; a recommendation module; after client data are collected to form groups, a group-based recommendation module is used for recommending commodities; using a historical purchase recommendation model in the process of browsing commodities by a client; the triggering module is used for pushing the commodities at the calculated time point or the set time point; the data acquisition module is used for acquiring data related to the client information; the data analysis module comprises a first data analysis module, a second data analysis module and a third data analysis module, calculates a potential purchased commodity list of a customer through an algorithm, and intercepts the promotion of commodities of the same brand and/or the same label in a shielding period; the commodity management module is used for replacing commodities on the display platform with potential commodities purchased by a customer; and the order settlement module is used for calculating the total amount of the commodities purchased by the customer and completing the payment function.
Description
Technical Field
The application relates to the technical field of internet, in particular to a hotel self-service shopping settlement platform and a hotel self-service shopping settlement system.
Background
It seems to be common today to take a mobile phone out of a convenience store, supermarket or restaurant, mall for payment. With the development of internet technology, barriers on and under the line are cut with mobile payment as a sharp edge, and mobile payment has penetrated the aspects of people's life. However, in some specific application scenarios, such as hotels, traditional meat and vegetable markets, and administrative institutions, there are still major disadvantages, and at the same time, there is considerable development space.
The existing plurality of commodities with the highest commodity common selection sales volume in the hotel are placed in hotel rooms for customers to select and consume, and the customers can settle accounts with house fees after returning. The customers who live in the hotel mostly spend time and energy and go up and down traveling personalities, and compared with common consumers, the demand of the customers is more personalized, diversified and qualified. The traditional hotel commodity sales mode is not suitable for the current hotel customers, the commodity recommendation algorithm adopted in the current shopping platform is mostly based on the consumption history of the user, and the recommendation mode has errors, for example, the user purchases a pot of coffee today, the shopping platform can continuously recommend coffee of the same brand according to the record, even the price is lower than that of the coffee purchased by the user, so that the user has a poor psychology, or the user does not need to continuously purchase coffee within a period of time, and finally the bad shopping experience of the user is formed; in addition, different from an online sales platform, the hotel shopping platform has the advantages that customers gather in a hotel and can acquire accurate basic information and dynamic information of the customers, and the characteristics are utilized, so that the hotel shopping platform is a great advantage for improving the sales volume of the hotel shopping platform.
Disclosure of Invention
The invention aims to overcome the defects of a shopping platform recommendation algorithm in the prior art and further improve the authenticity of customer data and the geographic clustering of customers in hotel application scenes. The system can make the most appropriate commodity pushing and develop an offline popularization mode based on different data reserves, finally effectively improve the sales volume of the hotel shopping platform, provide the most appropriate commodity for the client, improve the shopping experience of the client on the hotel self-service shopping platform, and achieve the win-win situation between the client and the platform.
A hotel self-service shopping settlement platform, comprising:
and the human-computer interface module is used for finishing the interaction between the client and the platform, and the main displayed contents comprise the on-sale commodities of the hotel self-service shopping settlement platform, the account information of the client and the platform notification.
The recommendation module generates a potential list for commodity recommendation by using the recommendation module based on clustering when the dynamic information of the client is blank; after client data are collected to form groups, a group-based recommendation module is used for recommending commodities; using a historical purchase recommendation model in the process of browsing commodities by a client;
the triggering module is used for pushing the commodities at the calculated time point or the set time point;
the data acquisition module is used for acquiring data related to the client information;
the data analysis module comprises a first data analysis module, a second data analysis module and a third data analysis module, calculates a potential purchased commodity list of a customer through an algorithm, and intercepts the promotion of commodities of the same brand and/or the same label in a shielding period;
the commodity management module is used for replacing commodities on the display platform with potential commodities purchased by a customer;
and the order settlement module is used for calculating the total amount of the commodities purchased by the customer and completing the payment function.
Further, the order calculation module includes a weight-based order calculation module, and/or a volume-based order calculation module, and/or a commodity-format-based order calculation module.
Further, the third data analysis module comprises a first time calculation module and a commodity division module; the first time calculation module acquires the data of the dynamic data acquisition module, calculates the first time of the passive triggering opportunity of the client based on the dynamic data of the client, and pushes the first time to the triggering module; the commodity dividing module divides the commodity attributes into disposable consumables, short-term consumables and long-term durable goods.
Further, the second data analysis module comprises a preference scoring module, a commodity labeling module, a customer grouping module and a commodity dividing module.
Further, the commodity labeling module obtains commodity evaluation contents and performs word segmentation on the commodity evaluation contents by manually adding some keywords which cannot be separated and are beneficial to label segmentation, removes unnecessary words in analysis, performs word frequency statistics on the keywords in the commodity, and obtains the keywords with the highest word frequency to obtain the commodity label.
Further, the volume-based order calculation module comprises a display module for placing commodities and an order calculation module for taking the commodities;
the calculation module is a box body, which is provided with an opening for picking and placing the commodity, a baffle is arranged outside the opening, the baffle and the box body are combined into a sealed state in the settlement process, and the customer takes the commodity which is about to be purchased off the display platform and then puts the commodity into the order calculation module based on the volume;
the calculation module is in a sealed state after the commodity is placed in the sealing state and the baffle is closed, quantitative air is injected in the sealed state, the pressure intensity after the air is injected is detected, customer order information is calculated according to the difference value of the pressure intensity before the air is injected and the pressure intensity after the quantitative air is injected, the customer order information is displayed on a display touch screen of the self-service settlement module, and an order payment module corresponding to the customer order information is popped up.
Furthermore, the historical purchase recommending module also comprises an evaluation screening module, an authority confirming module and a message pushing module;
the evaluation screening module extracts relevant evaluation aiming at the commodities browsed by the client;
the authority confirmation module calls promotion authority setting in the client static information, confirms that a client receiving promotion is a historical purchasing user according to the promotion authority setting, and confirms the online state and the current check-in state of the historical purchasing user;
the message pushing module pushes the historical purchasing clients which contain the current commodities or the commodities of the same category and are evaluated and have the promotion authority set to be yes to the current clients to provide consultation guidance, whether the historical purchasing users are online or not is displayed, and the current clients can select one of the historical purchasing clients to initiate commodity consultation.
According to the technical scheme, the method has the following advantages:
the method overcomes the defects of a shopping platform recommendation algorithm in the prior art, and further improves the authenticity of client data and the geographic clustering of clients in hotel application scenes. The method can effectively improve the sales volume of the hotel shopping platform, provides the most suitable commodity for the customer, improves the shopping experience of the customer on the hotel self-service shopping platform, and achieves the win-win purpose of the customer and the platform.
Drawings
FIG. 1 is a block framework diagram of one embodiment of a hotel self-service shopping settlement platform of the present application;
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The invention is further illustrated by the following figures and examples.
As shown in fig. 1, a hotel self-service shopping settlement platform includes a human-machine interface module, a recommendation module, a trigger module, a data acquisition module, a data analysis module, a commodity management module and an order settlement module.
The human-computer interface module is used for finishing the interaction between the customer and the platform, and the main displayed contents comprise the goods on sale of the self-service shopping settlement platform of the hotel, the account information of the customer and platform notification (preferential activities, new goods on shelf and the like). The human-computer interface module can be specifically an interface of a mobile terminal application, an interface of a smart television application and a small program interface of a hotel public number.
The recommendation module includes a cluster-based recommendation module, a group-based recommendation module, and a historical purchase recommendation module. When the dynamic information of the client is blank, a potential list is generated by using a recommendation module based on clustering to recommend commodities; after certain data of a client are collected to form a group, a group-based recommendation module is used for recommending commodities; the historical purchase recommendation model is used in the process of browsing goods by the customer. The client dynamic information blank specifically refers to that only static information is available, and acquisition of dynamic information such as order transaction, advertisement browsing and personal state acquisition does not occur.
A cluster-based recommendation module: and receiving the customer portrait pushed by the first analysis module, and pushing the preferred commodity corresponding to the current customer portrait into a potential purchased commodity list.
A packet-based recommendation module: the history-based recommending module pushes the commodities which are purchased by other customers in the group to which the current customer belongs but not purchased by the current customer into the potential purchased commodity list of the current customer.
A historical purchase recommendation module: the system comprises an evaluation screening module, an authority confirmation module and a message pushing module, wherein the evaluation screening module extracts relevant evaluations (including current commodities and commodities of the same category) aiming at commodities browsed by a client, the authority confirmation module calls promotion authority settings in static information of the client, confirms that a client receiving promotion is a historical purchasing user and confirms a platform online state and a current check-in state of the historical purchasing user, the message pushing module pushes the historical purchasing client containing current commodities or commodities of the same category and having the promotion authority settings to be the current client to provide consultation guidance and displays whether the historical purchasing user is online or not, the current client can select one of multiple historical purchasing clients to initiate commodity consultation, further, the commodity consultation comprises online consultation and offline consultation, and the current client can also initiate offline consultation on the historical purchasing client under the condition that the current check-in state of the historical purchasing user is in a hotel. After consulting the historical purchasing users, the dynamic data acquisition module in the data acquisition module automatically updates the scores of the historical purchasing users, and the specific updating mode is as follows: if the account number integral of the on-line consultation mode history purchasing user is increased by a first preset proportion, the commodity selling price is increased; if the account number integral of the offline popularization consultation mode historical purchasing user is increased by a second preset proportion and the commodity selling price is increased; after on-line consultation/off-line promotion is received, if the current client successfully purchases the commodity, additionally increasing a third preset proportion of commodity selling price to the account number integral of the historical purchasing user; the first preset proportion, the second preset proportion and the third preset proportion are real numbers which are larger than 0 and smaller than 1, and the first preset proportion is less than the second preset proportion and less than the third preset proportion. When the order settlement is carried out, the account number integral can be deducted from the commodity amount according to a fourth preset proportion to carry out payment.
Optionally, the historical purchase recommendation module calls a historical purchase record of a customer through the dynamic data acquisition module, the commodity dividing module acquires commodity attributes and commodity brands, the disposable consumables are normally pushed, a shielding period is set for short-term consumables and long-term durable goods, under the condition that the customer purchases certain categories of commodities, the commodities with the same brand and/or the commodities with the same tag are obtained through the commodity labeling module in the second data analysis module, and the promotion of the commodities with the same brand and/or the same tag is intercepted in the shielding period, so that the commodities which are not needed by the customer are prevented from being repeatedly promoted, and the purchase interest of the customer is improved.
The offline popularization consultation mode of the historical purchase recommendation module is based on the characteristic that the unique client geographic position of hotel shopping gathers, and aims at the problems that in the prior art, the online communication efficiency of sales personnel of a shopping platform is low, and the communication effect is not ideal, shopping experience is improved through face-to-face commodity popularization and recommendation commission return modes, the popularization enthusiasm is mobilized, the client interaction is improved, and the increase of commodity sales volume is promoted.
A triggering module: and the system is used for pushing the commodities at the calculated time point or the set time point.
Further, the trigger module comprises an active trigger module and a passive trigger module.
The initiative trigger module triggers the propelling movement based on the time/incident of setting for example triggers the propelling movement when the customer opens the door constantly, and specific propelling movement mode includes linkage intelligent TV broadcasts the advertisement, lights on the commodity display platform etc..
The passive triggering module triggers pushing at the first time based on the promoted commodities obtained by the third data analysis module, and the specific pushing mode can comprise linkage television broadcasting advertisements, and background lights on the commodity display platform and the like are lightened. The specific implementation mode is that if the sleep quality deviation of a client is judged, the light prompt of the commercial format of the milk beverage is lightened in the first 5 minutes of the next drinking water supplementing time point of the user is calculated; if the fact that the customer is used to sleep late is judged, a background light prompt of the coffee audio commercial format is turned on 5 minutes before the time point of next drinking water supplement of the user is calculated, and if the fact that the customer needs to purchase a special product is judged, a local special product commodity is inserted on the smart television and a background light prompt of the local special product is turned on before the customer is ready to leave a store; if the outdoor weather is judged to be severe in rain/haze, a vibration prompt for placing an umbrella/mask display lattice is initiated before the user is ready to exit; if the fact that the customer registers parking in the current travel is collected and the travel belongs to self-driving travel, when the customer watches the smart television program, automobile advertisements are pushed in the smart television/mobile terminal application, and a vibration prompt for placing sunglasses/air freshener display lattices is initiated.
And the data acquisition module is used for acquiring data related to the client information.
Further, the data acquisition module comprises a static data acquisition module and a dynamic data acquisition module. The static data acquisition module is used for taking a mobile phone number registered on a foreground at the time of check-in as an account number and acquiring basic information (native place, sex, age and the like) of a client by accessing a hotel foreground system, and supplementing and acquiring other static information when the client accesses a hotel wifi (for example, when the client accesses the hotel wifi for the first time, the client sets an account number password, height, weight, authority setting (such as promotion authority) and option preference and the like through the human-computer interface module), and simultaneously providing management functions such as member information modification and the like; the dynamic data acquisition module acquires dynamic information such as a check-in order record of a customer, an order record of a self-service shopping settlement platform, a point change record, a platform online state, a current check-in state, customer attributes obtained through analysis and the like. Optionally, the dynamic data acquisition module further acquires the personal state and the corresponding time of the client through a sensor in the intelligent device, for example, the entertainment time and the browsing record of the client can be acquired through the record of the intelligent television (the browsing record includes a television program browsing record and a commodity advertisement browsing record); the sleep starting time point and the sleep ending time point of the client can be collected through the intelligent mattress; the drinking time of the client can be collected through the intelligent water dispenser.
A data analysis module: a list of potential purchased items for the customer is calculated algorithmically. The data analysis module comprises a first data analysis module, a second data analysis module and a third data analysis module.
The first data analysis module obtains the client static information and dynamic information of the data acquisition module, synchronizes the data meeting the data warehouse specification into a hive table, cleans the original data and summarizes the data according to the analysis requirements (such as calculating age and constellation according to birth date, calculating client BMI according to height and weight, calculating sleep duration according to sleep starting time point and sleep ending time point, calculating drinking water frequency and the like), when the client only has static information, the client preference is predicted through a simple linear regression prediction model according to client age, gender, native place, constellation and the like, after the dynamic information is acquired, the client preference is predicted according to the program and duration watched by the client on a television, the lead actor type predicts the client preference through the simple linear regression prediction model, and summarizes the client static information, the dynamic information and the preference attribute obtained through calculation to obtain a client width table, and building a customer image based on the customer wide table, subdividing the requirements of different customers, and managing the customer images in a uniform customer labeling manner.
The second data analysis module comprises a preference scoring module, a commodity labeling module, a customer grouping module and a commodity dividing module.
The preference scoring module is used for analyzing the preference degree of the customers on the platform, the commodity labeling module is used for classifying commodities on the platform, and the customer grouping module is used for grouping the customers with similar preferences.
The preference scoring module acquires the browsing time t of the user browsing the ith commodity in the human-computer interface module through the dynamic acquisition moduleiAnd the number of browsing times CiThe total browsing time and the browsing times of the client are respectively set as tsAnd Cs. The preference score v of the customer for the item in the ithiIs composed of
The preference scoring system uses a 1 point score, with higher points representing a greater liking of the product by the customer. The customer-good preference table is thus obtained as follows:
| tea leaves | Coffee | Instant noodles | |
| Customer A | 0.9 | - | 0.85 |
| Customer B | - | 0.9 | 0.2 |
| Customer C | 0.95 | 0.8 | 0.9 |
| Client D | 0.3 | - | 0.4 |
This results in a customer-commodity matrix L: | M | × | N |, | M | represents a customer sequence, and | N | represents a commodity sequence. Element r in the matrixjkIndicating the rating of the kth item by the jth customer.
A commodity labeling module, which is used for manually adding some keywords which cannot be separated and are beneficial to label division, acquiring commodity evaluation content, performing word division on the commodity evaluation content, removing unnecessary words (tone words, commodity names and the like) in analysis, performing word frequency statistics on the keywords in the commodity, and taking the keyword with the highest word frequency to obtain a commodity label as follows:
the matrix W is factored into the product of the customer-label and label-goods matrices:
where R ═ M × | O |, | O | represents the number of tags, and element pjlIndicating the preference degree of the jth customer to the ith label; s ═ O | × | N |, element qklRepresenting kth article versus lth tagAnd (4) conformity.
Is a matrix after the matrix W is factored.
W and
the difference of (d) is:
wherein wjkFor each factor in W.
When W and
when the difference value is minimum, a target fitting result can be obtained. With pjlAnd q isklThe gradient is calculated as the independent variable in the table:
taking the calculation step as gamma, then:
p′jl＝pjl+2γejkqlk
q′lk＝qlk+2γejkpjl
assuming that preferences have been derived (customer, goods, preferences) to form a set T, we need to bias e on the elements in this setjkThe sum is as small as possible. While for unknown preferences, whileThe matrix R, S is calculated according to known terms, and then the result can be obtained. Finally determine ejkThe sum of the p values is a minimum value, and then all the p values of the clients can be obtainedjl，qlkAfter the value is obtained, the commodities can be pushed in high sequence according to the product of the two values.
The third data analysis module comprises a first time calculation module and a commodity division module.
The first time calculation module acquires the data of the dynamic data acquisition module, calculates the passive trigger opportunity (namely first time) of the client based on the dynamic data of the client, and pushes the first time to the trigger module. For example, the drinking frequency of the client can be calculated by acquiring the drinking time point of the client, and the first time of the next drinking time point is predicted based on the last drinking time point. The commodity dividing module divides the commodity attributes into disposable consumables, short-term consumables and long-term durable goods.
And the commodity management module is used for replacing the commodities on the display platform with potential commodities purchased by the customer.
Further, the commodity management module comprises a commodity information entry module and a commodity replacement module. The commodity information input module inputs commodity category, production date, quality guarantee period, weight, volume, inventory and amount information into the system; the commodity replacement module calls a potential commodity list of the client obtained by initial calculation/updating calculation of the recommendation module before the client enters the room and in the room cleaning process respectively, and replaces the original displayed commodities with potential commodity of the client by room cleaning personnel.
And the order settlement module is used for calculating the total amount of the commodities purchased by the customer and completing the payment function.
Further, the order settlement module comprises an order calculation module, an order payment module and a fault processing module. Optionally, the order calculation module comprises a weight-based order calculation module, and/or a volume-based order calculation module, and/or a commodity-lattice-based order calculation module.
The weight-based order calculation module comprises a display platform of a weight sensor and a self-service settlement module with a display touch screen. The method comprises the steps that commodities are placed on a display platform with weight calculation in a centralized mode, after the commodities are taken by a customer, customer order information is obtained through subtracting the total weight of the taken commodities from the original total weight, the customer order information comprises the quantity of the commodities taken by the customer, the commodity type, the corresponding amount of money of the commodities and the total amount of orders, the customer order information is displayed on a display touch screen of a self-service settlement module, and an order payment module corresponding to the customer order information is popped up.
The order calculation module based on the volume comprises a display module used for placing commodities and an order calculation module used for taking the commodities, and further the order calculation module is specifically a box body which is provided with an opening for taking and placing the commodities, a baffle is arranged outside the opening, the baffle and the box body are combined into a sealed state in the settlement process, and a customer takes off the commodities which are wanted to be purchased from the display platform and then puts the commodities into the order calculation module based on the volume. The order calculation module is in a sealed state after the commodity is placed in the order calculation module and the baffle is closed, quantitative air is injected in the sealed state, the pressure intensity after the air is injected is detected, the order information of a customer is calculated according to the difference value of the pressure intensity before the air is injected and the pressure intensity after the quantitative air is injected, the order information of the customer is displayed on a display touch screen of the self-service settlement module, and the order payment module corresponding to the order information of the customer is popped up.
The order calculation module based on the commodity lattice comprises a plurality of commodity display lattices arranged on a display platform, the commodity is independently placed in the display lattices, the display lattices are in a locked state before payment is completed, a customer pops up customer order information and a corresponding order payment module after selecting commodities which are wanted to be purchased on a display touch screen of a self-service settlement module, the display lattices are triggered to be unlocked after the customer payment is completed, and the customers can freely take the commodities purchased in the display lattices.
Further, the order payment module comprises a display touch screen of the self-service settlement module and a commodity settlement interface in the platform human-computer interface module. The payment interface for displaying the touch screen and the platform human-computer interface module comprises a balance payment mode, a code scanning payment mode, a face scanning payment mode, a hanging account house fee mode and a cash payment mode. The order payment module also supports a cancellation order or refund request initiated by the customer during the validity period.
And the fault processing module is used for automatically refunding according to the original payment interface when detecting that the platform has a fault. The detected platform fault can be the conditions that the unlocking of the display grid fails after the payment is successful, the platform has power failure accidentally or the platform has not completed orders due to accidental restarting and the like.
And after the customer pays successfully, updating and recording the order information of the customer through the dynamic data acquisition module.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made or substituted in a similar manner to the embodiments described herein by those skilled in the art without departing from the spirit of the invention or exceeding the scope thereof as defined in the appended claims.
Claims (10)
1. A hotel self-service shopping settlement platform, comprising:
the man-machine interface module is used for finishing the interaction between a client and the platform, and the main displayed contents comprise the on-sale commodities of the hotel self-service shopping settlement platform, the account information of the client and platform notification;
the recommendation module generates a potential list for commodity recommendation by using the recommendation module based on clustering when the client acquisition information is blank; after client data are collected to form groups, a group-based recommendation module is used for recommending commodities; using a historical purchase recommendation model in the process of browsing commodities by a client;
the triggering module is used for pushing the commodities at the calculated time point or the set time point;
the data acquisition module is used for acquiring data related to the client information;
the data analysis module comprises a first data analysis module, a second data analysis module and a third data analysis module, calculates a potential purchased commodity list of a customer through an algorithm, and intercepts the promotion of commodities of the same brand and/or the same label in a shielding period;
the commodity management module is used for replacing commodities on the display platform with potential commodities purchased by a customer;
and the order settlement module is used for calculating the total amount of the commodities purchased by the customer and completing the payment function.
2. The hotel self-service shopping settlement platform of claim 1, wherein the order calculation module comprises a weight-based order calculation module, and/or a volume-based order calculation module, and/or a price-based order calculation module.
3. The hotel self-service shopping settlement platform of claim 1, wherein the third data analysis module comprises a first time calculation module and a goods division module; the first time calculation module acquires the data of the dynamic data acquisition module, calculates the first time of the passive triggering opportunity of the client based on the dynamic data of the client, and pushes the first time to the triggering module; the commodity dividing module divides the commodity attributes into disposable consumables, short-term consumables and long-term durable goods.
4. The hotel self-service shopping settlement platform of claim 1, wherein the second data analysis module comprises a preference scoring module, a goods labeling module, a customer grouping module and a goods dividing module.
5. The hotel self-service shopping settlement platform according to claim 4, wherein the commodity labeling module obtains commodity evaluation contents and performs word segmentation on the commodity evaluation contents by manually adding some keywords which cannot be separated and are beneficial to label segmentation, removes unnecessary words and phrases in analysis, performs word frequency statistics on the keywords in the commodities and obtains the keywords with the highest word frequency to obtain commodity labels.
6. The hotel self-help shopping settlement platform according to claim 4, wherein the preference scoring module obtains the browsing time t of the user browsing the ith commodity through the dynamic acquisition moduleiAnd the number of browsing times CiThe total browsing time and the browsing times of the client are respectively set as tsAnd Cs. The preference score v of the customer for the item in the ithiIs composed of
The preference scoring system uses a 1 point score, with higher points representing a greater liking of the product by the customer.
7. The hotel self-service shopping settlement platform of claim 4,
the matrix W is factored into the product of the customer-label and label-goods matrices:
where R ═ M × | O |, | O | represents the number of tags, and element pjlIndicating the preference degree of the jth customer to the ith label; s ═ O | × | N |, element qklIndicating the conformity of the kth item to the l-th label.
The matrix W is a factorized matrix;
W and
the difference of (d) is:
wherein wjkFor each factor in W.
When W and
when the difference value of (A) is minimum, the target can be obtainedAnd (6) fitting results. With pjlAnd q isklThe gradient is calculated as the independent variable in the table:
taking the calculation step as gamma, then:
p′jl＝pjl+2γejkqlk
q′lk＝qlk+2γejkpjl
assuming that preferences have been derived (customer, goods, preferences) to form a set T, we need to bias e on the elements in this setjkThe sum is as small as possible. For unknown preferences, the result is obtained when the matrix R, S is calculated from the known terms. Finally determine ejkThe sum of the p values is a minimum value, and then all the p values of the clients can be obtainedjl，qlkAfter the value is obtained, the commodities can be pushed in high sequence according to the product of the two values.
8. The hotel self-service shopping settlement platform of claim 1, wherein the volume-based order calculation module comprises a display module for placing goods and an order calculation module for taking goods;
the calculation module is a box body, which is provided with an opening for picking and placing the commodity, a baffle is arranged outside the opening, the baffle and the box body are combined into a sealed state in the settlement process, and the customer takes the commodity which is about to be purchased off the display platform and then puts the commodity into the order calculation module based on the volume;
the calculation module is in a sealed state after the commodity is placed in the sealing state and the baffle is closed, quantitative air is injected in the sealed state, the pressure intensity after the air is injected is detected, customer order information is calculated according to the difference value of the pressure intensity before the air is injected and the pressure intensity after the quantitative air is injected, the customer order information is displayed on a display touch screen of the self-service settlement module, and an order payment module corresponding to the customer order information is popped up.
9. The hotel self-service shopping settlement platform according to claim 1, wherein the historical purchase recommendation module further comprises an evaluation screening module, a permission confirmation module and a message pushing module;
the evaluation screening module extracts relevant evaluation aiming at the commodities browsed by the client;
the authority confirmation module calls promotion authority setting in the client static information, confirms that a client receiving promotion is a historical purchasing user according to the promotion authority setting, and confirms the online state and the current check-in state of the historical purchasing user;
the message pushing module pushes the historical purchasing clients which contain the current commodities or the commodities of the same category and are evaluated and have the promotion authority set to be yes to the current clients to provide consultation guidance, whether the historical purchasing users are online or not is displayed, and the current clients can select one of the historical purchasing clients to initiate commodity consultation.
10. A hotel self-service shopping settlement system, wherein the hotel self-service shopping settlement system is configured to perform the functions of the hotel self-service shopping settlement platform of any one of claims 1-9.
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